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README.md

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 ---
-license: apache-2.0
+pretty_name: Optimized 478-Point 3D Facial Landmark Dataset
+language: en
+license:
+  - apache-2.0
+tags:
+  - computer-vision
+  - affective-computing
+  - facial-landmarks
+  - mediapipe
+  - emotion-recognition
+  - feature-extraction
+  - video-analysis
+  - optimized
+source_datasets:
+  - thnhthngchu/video-emotion
+task_categories:
+  - image-classification
+task_ids:
+  - multi-class-image-classification
+  - face-detection
+citation:
+  - "@misc{VideoEmotionDataset,
+    title={Video Emotion},
+    author={thnhthngchu},
+    year={2020},
+    publisher={Kaggle},
+    url={https://www.kaggle.com/datasets/thnhthngchu/video-emotion}
+    }"
+  - "@misc{MediaPipe,
+    title={MediaPipe},
+    author={Google Inc.},
+    year={2020},
+    url={https://mediapipe.dev/}
+    }"
 ---
+
+# Dataset Card for 478-Point Normalized 3D Facial Landmark Dataset
+
+## Dataset Description
+
+This dataset provides **pre-extracted, normalized 3D facial landmark features** derived from the **Video Emotion** dataset. It is optimized for efficient training of **emotion recognition** and **facial analysis models**, bypassing the need to process large raw video files.
+
+**License:** The extracted feature data in this Parquet file is licensed under **Apache 2.0**. Note that the original source video files may have separate licensing terms.
+
+Each entry (row in the Parquet) represents a single video frame and contains the corresponding emotion label along with 1434 features representing the x, y, z coordinates for 478 distinct facial landmarks, as generated by the MediaPipe Face Landmarker model.
+
+---
+
+## Data Fields and Structure
+
+The data is provided in a single Parquet file, typically named **`emotion_landmark_dataset.parquet`**.
+
+| Column Name      | Data Type          | Description                                                                                                       |
+| :--------------- | :----------------- | :---------------------------------------------------------------------------------------------------------------- |
+| `video_filename` | String             | The identifier of the original video file from which the frame was extracted.                                     |
+| `frame_num`      | Integer            | The sequential frame index within the original video file.                                                        |
+| `emotion`        | String/Categorical | The ground truth emotion label for this **clip**. **Classes include: Angry, Disgust, Fear, Happy, Neutral, Sad.** |
+| `x_0` to `x_477` | Float              | The normalized X coordinate (horizontal position) for each of the 478 landmarks (0.0 to 1.0).                     |
+| `y_0` to `y_477` | Float              | The normalized Y coordinate (vertical position) for each of the 478 landmarks (0.0 to 1.0).                       |
+| `z_0` to `z_477` | Float              | The normalized Z coordinate (depth, relative to the face center) for each of the 478 landmarks.                   |
+
+**Note on Coordinates:** Since the coordinates are **normalized** (0.0 to 1.0), they must be multiplied by the respective pixel width and height of the original frame to visualize them accurately.
+
+---
+
+## Data Collection and Processing
+
+### Source Video Details (Video Emotion Dataset)
+
+- **Source:** [Video Emotion](https://www.kaggle.com/datasets/thnhthngchu/video-emotion) (Kaggle User: thnhthngchu)
+- **Domain:** Facial expressions and affective computing, covering a range of scenarios.
+- **Labels:** Videos were originally labeled with clip-level emotional categories.
+- **License of Original Data:** Users must refer to the licensing terms specified by the original source dataset on Kaggle.
+
+### Feature Extraction Methodology
+
+The features were extracted using the **MediaPipe Face Landmarker** model.
+
+1.  **Frame Extraction:** Each video file was processed frame-by-frame.
+2.  **Landmark Detection:** For each frame, the 478 facial landmarks were detected.
+3.  **Normalization:** All coordinates (x, y, z) are normalized to the range [0.0, 1.0] relative to the bounding box of the face or the original frame dimensions.
+
+---
+
+## Usage Example and Visualization
+
+To ensure the coordinates have been extracted correctly and to demonstrate the data visually, please refer to the provided **`optimized-3d-facial-landmark-dataset-usage.ipynb`** file in the repository.
+
+This Jupyter Notebook contains a runnable Python example that **loads random video frames**, correctly denormalizes the coordinates using the frame's dimensions, and plots the 478 landmarks on the face.
+
+![Visualization](images/results.png)
+
+---
+
+## Potential Applications
+
+- **Transfer Learning:** Use the landmarks as input features for lightweight classifiers (e.g., LSTMs, simple MLPs) for emotion recognition.
+- **Biometrics:** Advanced facial tracking and identity verification research.
+- **Data Augmentation:** Analyze feature distribution for generating synthetic training data.
+
+---
+
+## Citation
+
+If you use this dataset in your research or project, please use the citation and acknowledge the original source data.
+
+- **Original Data Source:** [Video Emotion](https://www.kaggle.com/datasets/thnhthngchu/video-emotion) (Kaggle User: thnhthngchu)
+- **Extraction Framework:** Google Inc. (2020). MediaPipe. <https://mediapipe.dev/>
+
+- **This Dataset:**
+
+```bibtex
+@misc{pasindu_sewmuthu_abewickrama_singhe_2025,
+	author       = { Pasindu Sewmuthu Abewickrama Singhe },
+	title        = { Optimized_Video_Facial_Landmarks (Revision 7334b7d) },
+	year         = 2025,
+	url          = { https://huggingface.co/datasets/PSewmuthu/Optimized_Video_Facial_Landmarks },
+	doi          = { 10.57967/hf/6765 },
+	publisher    = { Hugging Face }
+}
+```